Overview

Brought to you by YData

Dataset statistics

Number of variables13
Number of observations1593
Missing cells0
Missing cells (%)0.0%
Duplicate rows219
Duplicate rows (%)13.7%
Total size in memory174.2 KiB
Average record size in memory112.0 B

Variable types

Categorical1
Numeric12

Alerts

type has constant value "Syrah"Constant
Dataset has 219 (13.7%) duplicate rowsDuplicates
citric acid is highly overall correlated with fixed acidity and 2 other fieldsHigh correlation
density is highly overall correlated with fixed acidityHigh correlation
fixed acidity is highly overall correlated with citric acid and 2 other fieldsHigh correlation
free sulfur dioxide is highly overall correlated with total sulfur dioxideHigh correlation
pH is highly overall correlated with citric acid and 1 other fieldsHigh correlation
total sulfur dioxide is highly overall correlated with free sulfur dioxideHigh correlation
volatile acidity is highly overall correlated with citric acidHigh correlation
citric acid has 132 (8.3%) zerosZeros

Reproduction

Analysis started2024-10-09 01:09:26.195875
Analysis finished2024-10-09 01:09:44.929066
Duration18.73 seconds
Software versionydata-profiling vv4.9.0
Download configurationconfig.json

Variables

type
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size24.9 KiB
Syrah
1593 

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters7965
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSyrah
2nd rowSyrah
3rd rowSyrah
4th rowSyrah
5th rowSyrah

Common Values

ValueCountFrequency (%)
Syrah 1593
100.0%

Length

2024-10-08T22:09:45.043862image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-10-08T22:09:45.134861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
syrah 1593
100.0%

Most occurring characters

ValueCountFrequency (%)
S 1593
20.0%
y 1593
20.0%
r 1593
20.0%
a 1593
20.0%
h 1593
20.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7965
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
S 1593
20.0%
y 1593
20.0%
r 1593
20.0%
a 1593
20.0%
h 1593
20.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7965
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
S 1593
20.0%
y 1593
20.0%
r 1593
20.0%
a 1593
20.0%
h 1593
20.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7965
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
S 1593
20.0%
y 1593
20.0%
r 1593
20.0%
a 1593
20.0%
h 1593
20.0%

fixed acidity
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.3195229
Minimum4.6
Maximum15.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2024-10-08T22:09:45.230257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum4.6
5-th percentile6.1
Q17.1
median7.9
Q39.2
95-th percentile11.8
Maximum15.9
Range11.3
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.7381441
Coefficient of variation (CV)0.20892353
Kurtosis1.1541798
Mean8.3195229
Median Absolute Deviation (MAD)1
Skewness0.98709748
Sum13253
Variance3.021145
MonotonicityNot monotonic
2024-10-08T22:09:45.348027image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7.2 67
 
4.2%
7.1 57
 
3.6%
7.8 53
 
3.3%
7.5 52
 
3.3%
7 50
 
3.1%
7.7 49
 
3.1%
6.8 46
 
2.9%
7.6 46
 
2.9%
8.2 45
 
2.8%
7.4 43
 
2.7%
Other values (86) 1085
68.1%
ValueCountFrequency (%)
4.6 1
 
0.1%
4.7 1
 
0.1%
4.9 1
 
0.1%
5 6
0.4%
5.1 4
 
0.3%
5.2 6
0.4%
5.3 3
 
0.2%
5.4 5
 
0.3%
5.5 1
 
0.1%
5.6 14
0.9%
ValueCountFrequency (%)
15.9 1
0.1%
15.6 2
0.1%
15.5 2
0.1%
15 2
0.1%
14.3 1
0.1%
14 1
0.1%
13.8 1
0.1%
13.7 2
0.1%
13.5 1
0.1%
13.4 1
0.1%

volatile acidity
Real number (ℝ)

HIGH CORRELATION 

Distinct142
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52747646
Minimum0.12
Maximum1.58
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2024-10-08T22:09:45.465142image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.12
5-th percentile0.27
Q10.39
median0.52
Q30.64
95-th percentile0.84
Maximum1.58
Range1.46
Interquartile range (IQR)0.25

Descriptive statistics

Standard deviation0.17905874
Coefficient of variation (CV)0.339463
Kurtosis1.238018
Mean0.52747646
Median Absolute Deviation (MAD)0.12
Skewness0.67445156
Sum840.27
Variance0.032062032
MonotonicityNot monotonic
2024-10-08T22:09:45.583389image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 47
 
3.0%
0.5 46
 
2.9%
0.43 43
 
2.7%
0.59 39
 
2.4%
0.36 38
 
2.4%
0.58 38
 
2.4%
0.4 37
 
2.3%
0.38 35
 
2.2%
0.49 35
 
2.2%
0.39 35
 
2.2%
Other values (132) 1200
75.3%
ValueCountFrequency (%)
0.12 3
 
0.2%
0.16 2
 
0.1%
0.18 10
0.6%
0.19 2
 
0.1%
0.2 3
 
0.2%
0.21 6
0.4%
0.22 6
0.4%
0.23 5
 
0.3%
0.24 13
0.8%
0.25 7
0.4%
ValueCountFrequency (%)
1.58 1
 
0.1%
1.33 2
0.1%
1.24 1
 
0.1%
1.185 1
 
0.1%
1.18 1
 
0.1%
1.13 1
 
0.1%
1.115 1
 
0.1%
1.09 1
 
0.1%
1.07 1
 
0.1%
1.04 3
0.2%

citric acid
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct80
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.27116133
Minimum0
Maximum1
Zeros132
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2024-10-08T22:09:45.889139image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.09
median0.26
Q30.42
95-th percentile0.6
Maximum1
Range1
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.1949545
Coefficient of variation (CV)0.71896128
Kurtosis-0.79113534
Mean0.27116133
Median Absolute Deviation (MAD)0.17
Skewness0.31731914
Sum431.96
Variance0.038007256
MonotonicityNot monotonic
2024-10-08T22:09:46.005792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 132
 
8.3%
0.49 68
 
4.3%
0.24 51
 
3.2%
0.02 49
 
3.1%
0.26 38
 
2.4%
0.1 35
 
2.2%
0.08 33
 
2.1%
0.01 33
 
2.1%
0.32 32
 
2.0%
0.21 32
 
2.0%
Other values (70) 1090
68.4%
ValueCountFrequency (%)
0 132
8.3%
0.01 33
 
2.1%
0.02 49
 
3.1%
0.03 30
 
1.9%
0.04 29
 
1.8%
0.05 20
 
1.3%
0.06 24
 
1.5%
0.07 22
 
1.4%
0.08 33
 
2.1%
0.09 30
 
1.9%
ValueCountFrequency (%)
1 1
 
0.1%
0.79 1
 
0.1%
0.78 1
 
0.1%
0.76 3
0.2%
0.75 1
 
0.1%
0.74 4
0.3%
0.73 3
0.2%
0.72 1
 
0.1%
0.71 1
 
0.1%
0.7 2
0.1%

residual sugar
Real number (ℝ)

Distinct91
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.53801
Minimum0.9
Maximum15.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2024-10-08T22:09:46.118172image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.9
5-th percentile1.6
Q11.9
median2.2
Q32.6
95-th percentile5.1
Maximum15.5
Range14.6
Interquartile range (IQR)0.7

Descriptive statistics

Standard deviation1.4098353
Coefficient of variation (CV)0.55548848
Kurtosis28.746371
Mean2.53801
Median Absolute Deviation (MAD)0.3
Skewness4.5559806
Sum4043.05
Variance1.9876357
MonotonicityNot monotonic
2024-10-08T22:09:46.234248image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 156
 
9.8%
1.8 129
 
8.1%
2.2 129
 
8.1%
2.1 128
 
8.0%
1.9 117
 
7.3%
2.3 109
 
6.8%
2.4 86
 
5.4%
2.5 84
 
5.3%
2.6 79
 
5.0%
1.7 76
 
4.8%
Other values (81) 500
31.4%
ValueCountFrequency (%)
0.9 2
 
0.1%
1.2 8
 
0.5%
1.3 5
 
0.3%
1.4 33
 
2.1%
1.5 30
 
1.9%
1.6 58
3.6%
1.65 2
 
0.1%
1.7 76
4.8%
1.75 2
 
0.1%
1.8 129
8.1%
ValueCountFrequency (%)
15.5 1
0.1%
15.4 2
0.1%
13.9 1
0.1%
13.8 2
0.1%
13.4 1
0.1%
12.9 1
0.1%
11 2
0.1%
10.7 1
0.1%
9 1
0.1%
8.9 1
0.1%

chlorides
Real number (ℝ)

Distinct153
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.087500942
Minimum0.012
Maximum0.611
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2024-10-08T22:09:46.346167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.012
5-th percentile0.054
Q10.07
median0.079
Q30.09
95-th percentile0.1264
Maximum0.611
Range0.599
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.047140269
Coefficient of variation (CV)0.53874013
Kurtosis41.583148
Mean0.087500942
Median Absolute Deviation (MAD)0.01
Skewness5.6728344
Sum139.389
Variance0.0022222049
MonotonicityNot monotonic
2024-10-08T22:09:46.460294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.08 66
 
4.1%
0.074 54
 
3.4%
0.076 51
 
3.2%
0.078 51
 
3.2%
0.084 49
 
3.1%
0.071 47
 
3.0%
0.077 47
 
3.0%
0.082 46
 
2.9%
0.075 45
 
2.8%
0.079 43
 
2.7%
Other values (143) 1094
68.7%
ValueCountFrequency (%)
0.012 2
 
0.1%
0.034 1
 
0.1%
0.038 2
 
0.1%
0.039 4
0.3%
0.041 4
0.3%
0.042 3
0.2%
0.043 1
 
0.1%
0.044 5
0.3%
0.045 4
0.3%
0.046 4
0.3%
ValueCountFrequency (%)
0.611 1
 
0.1%
0.61 1
 
0.1%
0.467 1
 
0.1%
0.464 1
 
0.1%
0.422 1
 
0.1%
0.415 3
0.2%
0.414 2
0.1%
0.413 1
 
0.1%
0.403 1
 
0.1%
0.401 1
 
0.1%

free sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.875706
Minimum1
Maximum72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2024-10-08T22:09:46.580069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q17
median14
Q321
95-th percentile35
Maximum72
Range71
Interquartile range (IQR)14

Descriptive statistics

Standard deviation10.466847
Coefficient of variation (CV)0.65929964
Kurtosis2.025542
Mean15.875706
Median Absolute Deviation (MAD)7
Skewness1.2517975
Sum25290
Variance109.55489
MonotonicityNot monotonic
2024-10-08T22:09:46.712527image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 136
 
8.5%
5 104
 
6.5%
10 79
 
5.0%
15 78
 
4.9%
12 75
 
4.7%
7 71
 
4.5%
9 62
 
3.9%
17 60
 
3.8%
11 59
 
3.7%
16 59
 
3.7%
Other values (50) 810
50.8%
ValueCountFrequency (%)
1 3
 
0.2%
2 1
 
0.1%
3 49
 
3.1%
4 41
 
2.6%
5 104
6.5%
5.5 1
 
0.1%
6 136
8.5%
7 71
4.5%
8 56
3.5%
9 62
3.9%
ValueCountFrequency (%)
72 1
 
0.1%
68 2
0.1%
66 1
 
0.1%
57 1
 
0.1%
55 2
0.1%
54 1
 
0.1%
53 1
 
0.1%
52 3
0.2%
51 4
0.3%
50 2
0.1%

total sulfur dioxide
Real number (ℝ)

HIGH CORRELATION 

Distinct144
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.391714
Minimum6
Maximum289
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2024-10-08T22:09:46.835381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile11
Q122
median38
Q362
95-th percentile112.4
Maximum289
Range283
Interquartile range (IQR)40

Descriptive statistics

Standard deviation32.885012
Coefficient of variation (CV)0.7088553
Kurtosis3.8502078
Mean46.391714
Median Absolute Deviation (MAD)18
Skewness1.5242909
Sum73902
Variance1081.424
MonotonicityNot monotonic
2024-10-08T22:09:46.949344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28 41
 
2.6%
24 36
 
2.3%
15 35
 
2.2%
18 35
 
2.2%
23 34
 
2.1%
14 33
 
2.1%
20 33
 
2.1%
31 32
 
2.0%
38 31
 
1.9%
27 30
 
1.9%
Other values (134) 1253
78.7%
ValueCountFrequency (%)
6 3
 
0.2%
7 4
 
0.3%
8 14
 
0.9%
9 14
 
0.9%
10 27
1.7%
11 26
1.6%
12 29
1.8%
13 28
1.8%
14 33
2.1%
15 35
2.2%
ValueCountFrequency (%)
289 1
0.1%
278 1
0.1%
165 1
0.1%
160 1
0.1%
155 1
0.1%
153 1
0.1%
152 1
0.1%
151 2
0.1%
149 1
0.1%
148 2
0.1%

density
Real number (ℝ)

HIGH CORRELATION 

Distinct436
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.213269
Minimum0.99007
Maximum100.369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2024-10-08T22:09:47.067727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.99007
5-th percentile0.9936
Q10.9956
median0.99675
Q30.99784
95-th percentile1
Maximum100.369
Range99.37893
Interquartile range (IQR)0.00224

Descriptive statistics

Standard deviation9.6819673
Coefficient of variation (CV)4.37451
Kurtosis96.109741
Mean2.213269
Median Absolute Deviation (MAD)0.00113
Skewness9.7849667
Sum3525.7375
Variance93.740491
MonotonicityNot monotonic
2024-10-08T22:09:47.183963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.9972 36
 
2.3%
0.9968 35
 
2.2%
0.9976 35
 
2.2%
0.998 29
 
1.8%
0.9962 28
 
1.8%
0.9978 26
 
1.6%
0.9964 25
 
1.6%
0.997 24
 
1.5%
0.9994 24
 
1.5%
0.9982 23
 
1.4%
Other values (426) 1308
82.1%
ValueCountFrequency (%)
0.99007 2
0.1%
0.9902 1
0.1%
0.99064 2
0.1%
0.9908 1
0.1%
0.99084 1
0.1%
0.9912 1
0.1%
0.9915 1
0.1%
0.99154 1
0.1%
0.99157 1
0.1%
0.9916 2
0.1%
ValueCountFrequency (%)
100.369 2
0.1%
100.315 3
0.2%
100.289 1
 
0.1%
100.242 2
0.1%
100.025 1
 
0.1%
100.024 1
 
0.1%
100.015 2
0.1%
100.012 1
 
0.1%
100.005 2
0.1%
10.032 1
 
0.1%

pH
Real number (ℝ)

HIGH CORRELATION 

Distinct89
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.3112241
Minimum2.74
Maximum4.01
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2024-10-08T22:09:47.296091image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum2.74
5-th percentile3.06
Q13.21
median3.31
Q33.4
95-th percentile3.57
Maximum4.01
Range1.27
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation0.15419004
Coefficient of variation (CV)0.046565873
Kurtosis0.82657442
Mean3.3112241
Median Absolute Deviation (MAD)0.1
Skewness0.19454285
Sum5274.78
Variance0.023774568
MonotonicityNot monotonic
2024-10-08T22:09:47.419289image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.3 57
 
3.6%
3.36 56
 
3.5%
3.26 53
 
3.3%
3.38 48
 
3.0%
3.39 48
 
3.0%
3.29 46
 
2.9%
3.32 44
 
2.8%
3.34 43
 
2.7%
3.28 42
 
2.6%
3.22 39
 
2.4%
Other values (79) 1117
70.1%
ValueCountFrequency (%)
2.74 1
 
0.1%
2.86 1
 
0.1%
2.87 1
 
0.1%
2.88 2
0.1%
2.89 4
0.3%
2.9 1
 
0.1%
2.92 4
0.3%
2.93 3
0.2%
2.94 4
0.3%
2.95 1
 
0.1%
ValueCountFrequency (%)
4.01 2
0.1%
3.9 2
0.1%
3.85 1
 
0.1%
3.78 2
0.1%
3.75 1
 
0.1%
3.74 1
 
0.1%
3.72 3
0.2%
3.71 4
0.3%
3.7 1
 
0.1%
3.69 4
0.3%

sulphates
Real number (ℝ)

Distinct96
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65805399
Minimum0.33
Maximum2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2024-10-08T22:09:47.537211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0.33
5-th percentile0.47
Q10.55
median0.62
Q30.73
95-th percentile0.93
Maximum2
Range1.67
Interquartile range (IQR)0.18

Descriptive statistics

Standard deviation0.1696915
Coefficient of variation (CV)0.25786866
Kurtosis11.715065
Mean0.65805399
Median Absolute Deviation (MAD)0.08
Skewness2.4304745
Sum1048.28
Variance0.028795206
MonotonicityNot monotonic
2024-10-08T22:09:47.653272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.6 69
 
4.3%
0.58 68
 
4.3%
0.54 68
 
4.3%
0.62 61
 
3.8%
0.56 60
 
3.8%
0.57 55
 
3.5%
0.53 51
 
3.2%
0.59 51
 
3.2%
0.55 50
 
3.1%
0.63 48
 
3.0%
Other values (86) 1012
63.5%
ValueCountFrequency (%)
0.33 1
 
0.1%
0.37 2
 
0.1%
0.39 6
 
0.4%
0.4 4
 
0.3%
0.42 5
 
0.3%
0.43 8
0.5%
0.44 16
1.0%
0.45 12
0.8%
0.46 18
1.1%
0.47 19
1.2%
ValueCountFrequency (%)
2 1
 
0.1%
1.98 1
 
0.1%
1.95 2
0.1%
1.62 1
 
0.1%
1.61 1
 
0.1%
1.59 1
 
0.1%
1.56 1
 
0.1%
1.36 3
0.2%
1.34 1
 
0.1%
1.33 1
 
0.1%

alcohol
Real number (ℝ)

Distinct60
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.422379
Minimum8.4
Maximum14.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2024-10-08T22:09:47.768702image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum8.4
5-th percentile9.2
Q19.5
median10.2
Q311.1
95-th percentile12.5
Maximum14.9
Range6.5
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.0639167
Coefficient of variation (CV)0.10208002
Kurtosis0.18589245
Mean10.422379
Median Absolute Deviation (MAD)0.7
Skewness0.8549597
Sum16602.85
Variance1.1319188
MonotonicityNot monotonic
2024-10-08T22:09:47.881864image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.5 139
 
8.7%
9.4 103
 
6.5%
9.8 78
 
4.9%
9.2 72
 
4.5%
10 67
 
4.2%
10.5 67
 
4.2%
9.6 59
 
3.7%
9.3 59
 
3.7%
11 59
 
3.7%
9.7 54
 
3.4%
Other values (50) 836
52.5%
ValueCountFrequency (%)
8.4 2
 
0.1%
8.5 1
 
0.1%
8.7 2
 
0.1%
8.8 2
 
0.1%
9 30
1.9%
9.05 1
 
0.1%
9.1 23
 
1.4%
9.2 72
4.5%
9.25 1
 
0.1%
9.3 59
3.7%
ValueCountFrequency (%)
14.9 1
 
0.1%
14 7
0.4%
13.6 4
0.3%
13.5 1
 
0.1%
13.4 3
 
0.2%
13.3 3
 
0.2%
13.2 1
 
0.1%
13.1 2
 
0.1%
13 6
0.4%
12.9 9
0.6%

quality
Real number (ℝ)

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6340239
Minimum3
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size24.9 KiB
2024-10-08T22:09:47.978998image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q15
median6
Q36
95-th percentile7
Maximum8
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.80810744
Coefficient of variation (CV)0.14343344
Kurtosis0.29728202
Mean5.6340239
Median Absolute Deviation (MAD)1
Skewness0.22230465
Sum8975
Variance0.65303763
MonotonicityNot monotonic
2024-10-08T22:09:48.070833image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
5 681
42.7%
6 633
39.7%
7 198
 
12.4%
4 53
 
3.3%
8 18
 
1.1%
3 10
 
0.6%
ValueCountFrequency (%)
3 10
 
0.6%
4 53
 
3.3%
5 681
42.7%
6 633
39.7%
7 198
 
12.4%
8 18
 
1.1%
ValueCountFrequency (%)
8 18
 
1.1%
7 198
 
12.4%
6 633
39.7%
5 681
42.7%
4 53
 
3.3%
3 10
 
0.6%

Interactions

2024-10-08T22:09:43.231195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:26.498368image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:27.890989image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:29.411785image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:30.934626image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:32.329530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:33.653850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:35.011343image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:36.596332image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:37.920195image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:39.286922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:40.670661image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:43.322223image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:26.621008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:27.987720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:29.513039image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:31.029928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:32.421420image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:33.755565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:35.121167image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:36.687354image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:38.013062image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:39.387883image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:40.852761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:43.420733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:26.720997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:28.091155image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:29.623914image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:31.137543image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:32.522237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:33.859878image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:35.220174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:36.789351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:38.115483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:39.502008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:41.035312image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:43.514893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:26.815273image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:28.201672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:29.720141image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:31.246280image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:32.616824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:33.959100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:35.318927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:36.881247image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:38.212829image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:39.610692image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:41.209997image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:43.616832image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:26.919271image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:28.370090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:29.817173image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:31.366237image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:32.714709image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:34.060815image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:35.425565image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:36.984644image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:38.319386image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:39.720851image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:41.405351image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:43.712739image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:27.016345image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:28.475996image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:29.914310image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:31.468671image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:32.815990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:34.164177image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:35.524638image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:37.080784image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:38.419003image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:39.823710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:41.777828image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:43.807305image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:27.128518image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:28.582324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:30.015200image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:31.573392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:32.920028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:34.265075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:35.623755image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:37.184574image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:38.519804image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:39.929881image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:41.960374image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:43.902030image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:27.238889image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:28.685790image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:30.111542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:31.674540image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:33.016208image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:34.373222image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:35.728788image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:37.282544image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:38.620499image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:40.031059image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:42.154809image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:43.998983image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:27.336215image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:28.807571image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:30.203765image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:31.782723image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:33.108373image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:34.466894image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:35.823267image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:37.370842image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:38.718498image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:40.129854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:42.339990image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:44.091333image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:27.437428image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:28.922220image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:30.302530image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:31.879131image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:33.203720image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:34.564321image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:35.917972image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:37.469064image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:38.815266image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:40.228291image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:42.536582image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:44.195323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:27.547507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:29.029405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:30.402554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:31.986513image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:33.306099image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:34.668753image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:36.020383image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:37.578568image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:38.918810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:40.334346image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:42.731838image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:44.497184image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:27.800586image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:29.311191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:30.840121image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:32.231309image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:33.560353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:34.912880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:36.500265image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:37.826598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:39.181391image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:40.577840image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-10-08T22:09:43.052554image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-10-08T22:09:48.144821image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
alcoholchloridescitric aciddensityfixed acidityfree sulfur dioxidepHqualityresidual sugarsulphatestotal sulfur dioxidevolatile acidity
alcohol1.000-0.2830.098-0.461-0.066-0.0820.1800.4790.1190.206-0.260-0.224
chlorides-0.2831.0000.1110.4110.2480.003-0.231-0.1890.2160.0230.1330.160
citric acid0.0980.1111.0000.3510.661-0.074-0.5480.2150.1800.3330.012-0.610
density-0.4610.4110.3511.0000.624-0.040-0.312-0.1760.4230.1630.1310.024
fixed acidity-0.0660.2480.6610.6241.000-0.172-0.7060.1160.2270.215-0.085-0.277
free sulfur dioxide-0.0820.003-0.074-0.040-0.1721.0000.114-0.0570.0720.0460.7900.019
pH0.180-0.231-0.548-0.312-0.7060.1141.000-0.044-0.094-0.083-0.0120.234
quality0.479-0.1890.215-0.1760.116-0.057-0.0441.0000.0320.376-0.200-0.383
residual sugar0.1190.2160.1800.4230.2270.072-0.0940.0321.0000.0400.1430.029
sulphates0.2060.0230.3330.1630.2150.046-0.0830.3760.0401.000-0.001-0.326
total sulfur dioxide-0.2600.1330.0120.131-0.0850.790-0.012-0.2000.143-0.0011.0000.091
volatile acidity-0.2240.160-0.6100.024-0.2770.0190.234-0.3830.029-0.3260.0911.000

Missing values

2024-10-08T22:09:44.621916image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-10-08T22:09:44.789111image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
1632Syrah7.40.700.001.90.07611.034.00.99783.510.569.45
1633Syrah7.80.880.002.60.09825.067.00.99683.200.689.85
1634Syrah7.80.760.042.30.09215.054.00.99703.260.659.85
1635Syrah11.20.280.561.90.07517.060.00.99803.160.589.86
1636Syrah7.40.700.001.90.07611.034.00.99783.510.569.45
1637Syrah7.40.660.001.80.07513.040.00.99783.510.569.45
1638Syrah7.90.600.061.60.06915.059.00.99643.300.469.45
1639Syrah7.30.650.001.20.06515.021.00.99463.390.47107
1640Syrah7.80.580.022.00.0739.018.00.99683.360.579.57
1641Syrah7.50.500.366.10.07117.0102.00.99783.350.8010.55
typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality
3221Syrah6.60.7250.207.80.07329.079.00.997703.290.549.25
3222Syrah6.30.5500.151.80.07726.035.00.993143.320.8211.66
3223Syrah5.40.7400.091.70.08916.026.00.994023.670.5611.66
3224Syrah6.30.5100.132.30.07629.040.00.995743.420.75116
3225Syrah6.80.6200.081.90.06828.038.00.996513.420.829.56
3226Syrah6.20.6000.082.00.09032.044.00.994903.450.5810.55
3227Syrah5.90.5500.102.20.06239.051.00.995123.520.7611.26
3228Syrah6.30.5100.132.30.07629.040.00.995743.420.75116
3229Syrah5.90.6450.122.00.07532.044.00.995473.570.7110.25
3230Syrah6.00.3100.473.60.06718.042.00.995493.390.66116

Duplicate rows

Most frequently occurring

typefixed acidityvolatile aciditycitric acidresidual sugarchloridesfree sulfur dioxidetotal sulfur dioxidedensitypHsulphatesalcoholquality# duplicates
22Syrah6.70.4600.241.70.07718.034.00.994803.390.6010.664
52Syrah7.20.3600.462.10.07424.044.00.995343.400.851174
63Syrah7.20.6950.132.00.07612.020.00.995463.290.5410.154
81Syrah7.50.5100.021.70.08413.031.00.995383.360.5410.564
5Syrah6.00.5000.001.40.05715.026.00.994483.360.459.553
12Syrah6.40.6400.211.80.08114.031.00.996893.590.669.853
39Syrah7.00.6500.022.10.0668.025.00.997203.470.679.563
40Syrah7.00.6900.072.50.09115.021.00.995723.380.6011.363
60Syrah7.20.6300.001.90.09714.038.00.996753.370.58963
104Syrah7.80.6000.262.00.08031.0131.00.996223.210.529.953